AI RESEARCH
RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
arXiv CS.LG
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ArXi:2604.15945v1 Announce Type: cross Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or domain-specific knowledge. Nonetheless, current models still produce closed-domain hallucinations and generate content that is uned by the retrieved context. Current detection approaches typically treat hallucination as a post-hoc problem, relying on black-box consistency checks or probes over frozen internal representations.